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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
Arjuna Subject : -
Articles 217 Documents
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The Impact of Algorithms on Decision-Making in Daily Life: A Polling Study of Technology Users Dwi Yuniarto; Akbar, Yopi Hidayatul; Abd. Rahman, Aedah; Herdiana, Dody
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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Abstract

Algorithms have become an integral part of everyday life, particularly in entertainment, shopping, and navigation. This study examines how algorithms influence individual decision-making. Data were collected through an online poll involving 200 respondents, selected using a statistical sampling method. The results indicate that 55% of respondents perceive algorithms as having a significant influence on their decisions, while 28% report a moderate impact. A confidence interval analysis (95%) has been included to ensure statistical accuracy. The study highlights the importance of digital literacy in mitigating algorithmic bias and suggests future research on how socio-cultural factors shape algorithmic perceptions. This research contributes to understanding the extent of algorithmic influence on daily decision-making and raises user awareness of technology’s impact. The implications include the importance of digital literacy to mitigate dependency and bias in algorithm usage and the potential to develop more transparent and ethical algorithmic systems. Future research could explore the relationship between users' awareness of algorithms and their behaviors in various contexts and evaluate ways to enhance public understanding of how algorithms function in the evolving digital ecosystem.
Implementation and Performance Analysis of PVD Method in Concealing Encrypted Data on Images Hanif, Ardhan; Astuti, Nur Rochmah Dyah Puji; Aribowo, Eko
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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Abstract

This research aims to secure text data by combining steganography and cryptography. The Pixel Value Differencing (PVD) method allows for higher data insertion capacity with minimal distortion, thereby increasing resistance to steganalysis. However, the PVD steganography method is vulnerable to variation in image areas and to the accuracy of Pixel Difference Histogram (PDH) analysis. In addition, this method is susceptible to statistical tools such as the chi-square and RS, which can be used to analyze the distribution of pixel value differences, allowing data to be detected. To address the limitations of the PVD method, we employed a cryptographic technique called XOR-VLSB, which combines XOR as the primary encryption method, Vigenère Cipher for key generation, and Least Significant Bit (LSB) for key embedding. The results showed that the fully encrypted data could be recovered and had good image quality, as indicated by the metric results, which included a low MSE value, a PSNR above 35 dB, and an SSIM value close to 1. In this study, the process of encrypting text data still uses a simple encryption algorithm, namely XOR. Future research may involve replacing cryptographic algorithms with AES, which offers stronger protection and better resistance to advanced security threats.
Towards Fair and Efficient Timetabling: A Genetic Algorithm Model Integrating Lecturer Day-Off Requests Khaeroni, Khaeroni; Muqdamien, Birru; Hestiningtyas, Ajeng
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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Abstract

This study tackles the complex challenge of lecture timetabling by incorporating lecturer day-off preferences, a crucial constraint often neglected in traditional scheduling methods. Given the NP-hard nature of the problem and the need for scalable solutions, a Genetic Algorithm (GA) was employed with a population size of 10, a crossover probability of 0.70, a mutation probability of 0.20, and a maximum generation of 10000. The proposed GA-based method, implemented using PHP and MySQL, is applied to a real-world scenario involving 25 courses, 22 lecturers, and six classrooms over a 5-day weekly schedule at the Faculty of Education and Teacher Training for the Even Semester of the 2023/2024 Academic Year. Experimental results, validated through the Mann-Whitney test, show that incorporating lecturer preferences enhances scheduling flexibility without significantly increasing computational time. Comparative analysis with Simulated Annealing and Tabu Search demonstrates the competitive performance of the GA-based method in optimizing lecture schedules. This study provides a practical solution for educational institutions seeking to improve their timetabling processes.
Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network Ramadhan, Ade Umar; Siregar, Maria Ulfah; Nafisah, Syifaun; Anshari, Muhammad; Ndungi, Rebeccah; Mulyawan, Rizki; Nurochman, Nurochman; Gunawan, Eko Hadi
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5129

Abstract

This study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study's results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry.
IT Infrastructure Assessment using the COBIT 2019 Framework Rifa'i, Aulia Faqih; Sumarsono; Muhammad Fauzan Al Baihaqi; Yazid Azfa Yasa
IJID (International Journal on Informatics for Development) Vol. 12 No. 2 (2023): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5152

Abstract

The Admission Office is responsible for student enrollment, and since 2013, the admission process at UIN Sunan Kalijaga has been supported by information technology. To assess the current state of the IT infrastructure in this university, the COBIT 2019 Framework was used. This study identifies five key domains in need of improvement: APO12 (manage risk), which focuses on managing IT-related risks within an organization, BAI10 (manage configuration), to ensure that IT services are delivered efficiently and effectively, DSS02 (manage service requests & incidents), involves the process of providing quick and efficient responses to user requests and handling various incidents, DSS03 (manage problems), to provide timely and effective support to consumers, ensuring their issues are addressed, their needs are met, and DSS04 (manage continuity), to ensure that the organization can respond effectively to incidents and disruptions, minimizing downtime and maintaining business continuity. The results showed that the capability levels for these domains in UIN Sunan Kalijaga were at Level 1, while the target was Level 4, leading to a capability gap of 3. The gap indicates that considerable effort is required to improve and achieve the desired level of maturity, and this research proposes some recommendations to improve the IT infrastructure.
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Mualifah, Laily Nissa Atul; Wijayanto, Hari
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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Abstract

This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.
A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
IJID (International Journal on Informatics for Development) 2025
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.

Page 22 of 22 | Total Record : 217

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